In this paper, we aim at identifying uncertainty cues in Hungarian social media texts. We present our machine learning based uncertainty detector which is based on a rich features set including lexical, morphological, syntactic, semantic and discourse-based features, and we evaluate our system on a small set of manually annotated social media texts. We also carry out cross-domain and domain ada...

A method for generating a machine translation (MT) dictionary from parallel texts is described. This method utilizes both statistical information and linguistic information to obtain corresponding words or phrases in parallel texts. By combining these two types of information, translation pairs which cannot be obtained by a linguistic-based method can be extntcted. Over 70% accurate translation...

This paper discusses aspects of the planning of explanatory texts for logic based systems. It presents a method for deriving Natural Language text plans from Natural Deduction-based structures. This approach allows for the planning of explanatory texts in a general-purpose logic based system framework, ensuring a greater degree of portability across domains.

the main purpose of this research is investigation of foundations and principles of islamic ethics concerning the relationship of teacher with students. to achieve this goal, the analytical - inferential method was used. based on the results, at first eight ethical principles related to the relationship of teacher with students were deducted from religious and educational texts of islam. these ...

People are generating more and more short texts. There is an urgent demand to classify short texts into different domains. Due to the shortness and sparseness of short texts, conventional methods based on Vector Space Model (VSM) have limitations. To tackle the data scarcity problem, we propose a new model to directly measure the correlation between a short text instance and a domain instead of...

this research aimed to review the use of second-person reference in advertisement translation, work out the general rules, and provide guidance to translators. using second-person reference is common in the advertising discourse. addressing audiences directly involves their attention and in this way enhances their memorization of the advertised message. second-person reference can be realized v...

In this paper we show how a vector representation of words based on word embeddings can help to improve the results in tasks focused on the semantic similarity of texts. Thus we have experimented with two methods that rely on the vector representation of words to calculate the degree of similarity of two texts, one based on the aggregation of vectors and the other one based on the calculation o...

Author identification is a text categorization task with applications in intelligence, criminal law, computer forensics, etc. Usually, in such cases there is shortage of training texts. In this paper, we propose the use of second order tensors for representing texts for this problem, in contrast to the traditional vector space model. Based on a generalization of the SVM algorithm that can handl...

This paper studies the problem of emotion classification in microblog texts. Given a microblog text which consists of several sentences, we classify its emotion as anger, disgust, fear, happiness, like, sadness or surprise if available. Existing methods can be categorized as lexicon based methods or machine learning based methods. However, due to some intrinsic characteristics of the microblog ...